Understanding Weekly COVID-19 Concerns through Dynamic Content-Specific LDA Topic Modeling

Mohammadzaman Zamani, H. Andrew Schwartz, Johannes Eichstaedt, Sharath Chandra Guntuku, Adithya Virinchipuram Ganesan, Sean Clouston, Salvatore Giorgi


Abstract
The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media.We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including social mobility and unemployment rate.
Anthology ID:
2020.nlpcss-1.21
Volume:
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
193–198
Language:
URL:
https://www.aclweb.org/anthology/2020.nlpcss-1.21
DOI:
10.18653/v1/2020.nlpcss-1.21
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.nlpcss-1.21.pdf